2D/3D image fusion may be employed on a patient for radiological monitoring of interventions (e.g., treatment processes). For example, three-dimensional image data sets (e.g., CT image data sets and/or magnetic resonance image data sets) captured prior to the intervention are superimposed on two-dimensional real-time projection images (e.g., fluoroscopy images). To this end, an underlying 2D/3D registration is used. The accuracy of the superimposition directly influences the dependability of the additional information that the three-dimensional image provides.
In clinical practice, a highly accurate 2D/3D superimposition may be initially available since, for example, automatic and/or manual registration algorithms have been employed for the first projection image captured. However, there are a number of causes that may result in inaccuracies during the procedure (e.g., at least the period of time wherein projection images are captured). One of the inaccuracies relates to movement in the captured target area (e.g., the movement of a patient).
An operator involved in the process may manually initiate a new 2D/3D registration if the erroneous superimposition becomes clearly visible and affects the treatment process. The correction process that is implemented is the same registration procedure that was initially performed (e.g., using the most-recently captured projection image in order to achieve a registration with the three-dimensional image data set). However, the physician may be distracted from performing the process by the movement correction.
One solution to the problem of movement is to automatically track the movement of the patient, or at least of the captured target area, during the two-dimensional projection image capture (e.g., “on-the-fly” movement correction. Knowledge of the temporal progression of the movement may also permit predictions for further projection images. However, there is no simple solution for tracking patient movement in projection images. One reason that no such tracking is presently available is that an image datum (e.g., image value) of an X-ray projection image (e.g., a.k.a. attenuation value) is an integral of the attenuation coefficients of different depths along the path of the X-ray beam. Thus, the depth information is lost in the projection image. As a result, derivation of the three-dimensional movement from X-ray projection image sequences by tracking algorithms is difficult.
One approach is to carry out the process of 2D/3D registration as described above for each projection image captured. However, conventional methods are not sufficiently fast to permit real-time tracking of the movement and, therefore, real-time compensation for the movement. Further discussion may be found in an article by A. Kubias et al. entitled “Extended Global Optimization Strategy for Rigid 2D/3D Image Registration,” in: W. G. Kropatsch, M. Kampel and A. Hanbury (Eds.): CAIP 2007, LNCS 4673, pp. 759-767, 2007.
Another approach to movement compensation involves tracking special devices (e.g., catheters) that move conjointly with an organ of a patient. In such an approach, the depth information may be obtained only when two or more projection images from different projection angles are available. Further discussion may be found in an article by P. Markelj et al. entitled “A review of 3D/2D registration methods for image-guided interventions,” Medical Image Analysis, 2012, 16, pp. 642-661.
Another approach is to obtain the depth information from the initial registration. The target area is broken down into different depth layers. A standard tracking method is associated with movement estimation effective at depth. The 3D movement is automatically detected and compensated for, thereby facilitating a correct superimposition. Further discussion may be found in an article by J. Wang et al. entitled “Depth-Layer-Based Patient Motion Compensation for the Overlay of 3D Volumes onto X-Ray Sequences,” in: H.-P. Meinzer et al. (publisher), Bildverarbeitung für die Medizin 2013, Informatik aktuell, Springer Verlag Berlin Heidelberg 2013, pp. 128-133. However, in this approach, normal tracking algorithms are not suitable for X-ray projection images.